Course curriculum

Introduction

Introduction to Spark

Resilient Distributed Datasets

Advanced RDDs: Pair Resilient Distributed Datasets

Java and Spark

7

PageRank: Ranking Search Results

8

Spark SQL

9

MLlib in Spark: Build a recommendations engine

10

Spark Streaming

11

Graph Libraries

You, This Course and Us

What does Donald Rumsfeld have to do with data analysis?
Why is Spark so cool?
An introduction to RDDs - Resilient Distributed Datasets
Built-in libraries for Spark
Installing Spark
The PySpark Shell
Transformations and Actions
See it in Action : Munging Airlines Data with PySpark - I
[For Linux/Mac OS Shell Newbies] Path and other Environment Variables
Downloads

RDD Characteristics: Partitions and Immutability
RDD Characteristics: Lineage, RDDs know where they came from
What can you do with RDDs?
Create your first RDD from a file
Average distance travelled by a flight using map() and reduce() operations
Get delayed flights using filter(), cache data using Persist()
Average flight delay in one-step using aggregate()
Frequency histogram of delays using countByValue()
See it in Action : Analyzing Airlines Data with PySprak - II
Downloads

Special Transformations and Actions
Average delay per airport, use reduceByKey(), MapValues() and Join()
Average delay per airport in one step using combineByKey()
Get the top airports by delay using sortBy()
Lookup airport descriptions using lookup(), collectAsMap(). broadcast()
See it in Action : Analyzing Airlines Data with PySpark - III
Downloads

Get information from individual processing nodes using accumulators
See it in Action : Using an Accumulator variable
Long running programs using spark-submit
See it in Action : Running a Python script with Spark-Submit
Behind the scenes: What happens when a Spark script runs?
Running MapReduce operations
See it in Action : MapReduce with Spark
Downloads

Introduction to streaming
Implement stream processing in Spark using Dstreams
Stateful transformations using sliding windows
See it in Action : Spark Streaming
Downloads

The Marvel social network using Graphs
Downloads

Course Description

What will I learn?

Use Spark for a variety of analytics and Machine Learning tasks.

Implement complex algorithms like PageRank or Music Recommendations.

Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings.

Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX.

About the course

This course is taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data. Get your data to fly using Spark for analytics, machine learning and data science.

Let’s parse that.

What's Spark? If you are an analyst or a data scientist, you're used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.

Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.

Machine Learning and Data Science: Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.

What's Covered

Lot's of cool stuff!

Music Recommendations using Alternating Least Squares and the Audioscrobbler dataset.